Abstract
Generative machine learning models offer a promising way to efficiently amplify classical Monte Carlo generators’ statistics for event simulation and generation in particle physics. Given the already high computational cost of simulation and the expected increase in data in the high-precision era of the LHC and at future colliders, such fast surrogate simulators are urgently needed. This contribution presents a status update on simulating particle showers in high granularity calorimeters for future colliders. Building on prior work using Generative Adversarial Networks (GANs), Wasserstein-GANs, and the information-theoretically motivated Bounded Information Bottleneck Autoencoder (BIB-AE), we further improve the fidelity of generated photon showers. The key to this improvement is a detailed understanding and optimisation of the latent space. The richer structure of hadronic showers compared to electromagnetic ones makes their precise modeling an important yet challenging problem. We present initial progress towards accurately simulating the core of hadronic showers in a highly granular scintillator calorimeter.
Highlights
Simulation in high energy physics links our deep understanding of fundamental theories and the behaviour of detectors to experimental data
We previously investigated the use of generative models for simulating electromagnetic showers in a 30 × 30 × 30 cubic region of the SiW electromagnetic calorimeter proposed for the International Large Detector (ILD) [15]
This work builds on the generative setup introduced in Ref. [15] and i) improves the fidelity of electromagnetic showers and ii) presents first steps towards simulating hadronic showers
Summary
Simulation in high energy physics links our deep understanding of fundamental theories and the behaviour of detectors to experimental data. A large number of potential network architectures such as Generative Adversarial Networks (GANs) [3], Variational Autoencoders (VAEs) [4] and models based on autoregressive flows [5] were proposed for this task. Since the initial idea to use GANs to simulate calorimeter showers in high energy physics [6], several extensions and improvements were suggested [7,8,9,10,11,12,13,14]. There, we used three generative approaches: a standard GAN; a GAN based on the Wasserstein distance [16]; and the Bounded Information Bottleneck Autoencoder (BIB-AE) architecture [17] that unifies features of GANs and VAEs. there is still a long way to go before the fidelity and versatility required for real-world application is reached. An overview of results for both generative tasks is presented in Sec. 4, and Sec. 5 concludes this work
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